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A COMPREHENSIVE OVERVIEW OF SOFT COMPUTING BASED MPPT TECHNIQUES FOR PARTIAL SHADING CONDITIONS IN PV SYSTEMS

Year 2019, Volume: 7 Issue: 4, 926 - 939, 19.12.2019
https://doi.org/10.21923/jesd.570887

Abstract

Nowadays, solar or
photovoltaic energy is the most commonly used renewable energy resources in the
world. Despite its advantages such as freely available, low maintenance cost,
pollution-free, inexhaustible, and reliable, its low conversion efficiency is a
major drawback. To increase the efficiency of the photovoltaic system, all
photovoltaic modules in the array must be operated at maximum power point.
Therefore, maximum power point tracking technique is used for predicting and
tracking the maximum power point. In the literature, maximum power point
tracking techniques are generally classified as soft computing and
conventional. Soft computing techniques are more preferred from both of them,
because they can accurately track maximum power point of photovoltaic systems.
In this study, an extensive review of soft computing based maximum power point
tracking techniques under partial shading conditions until today is presented.
The techniques are compared from the point of photovoltaic array dependency,
sensors required, tracking efficiency, tracking speed, algorithm complexity,
and oscillation around maximum power point.

References

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  • Jiang, L. L., Maskell, D. L., & Patra, J. C., 2013. A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions. Energy and Buildings, 58, 227-236.
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PV SİSTEMLERDE KISMİ GÖLGELENME KOŞULLARINDA ESNEK HESAPLAMA TABANLI MAKSİMUM GÜÇ NOKTASI İZLEME TEKNİKLERİNİN KARŞILAŞTIRILMASI

Year 2019, Volume: 7 Issue: 4, 926 - 939, 19.12.2019
https://doi.org/10.21923/jesd.570887

Abstract

Günümüzde güneş ya da fotovoltaik enerji yenilenebilir enerji
kaynakları arasında yaygın olarak kullanılmaktadır. Güneş enerjisi, maliyetsiz,
atmosfer dostu,  işletme ve bakım
maliyetinin az olması ve evrensel olarak her yerde bulunması gibi avantajlarına
rağmen, düşük enerji verimliliği en büyük dezavantajıdır. Fotovoltaik
sistemlerin verimliliğini arttırabilmek için fotovoltaik dizideki modüller
maksimum güç noktalarında çalıştırılmalıdır. Bu nedenle, maksimum güç noktasını
tahmin etmek ve izlemek için maksimum güç noktası izleme teknikleri kullanılır.
Literatürde, maksimum güç noktası izleme teknikleri genellikle esnek hesaplama
ve klasik teknikler olmak üzere sınıflandırılır. Ancak, maksimum güç noktasını
doğru bir şekilde takip edebildikleri için esnek hesaplama teknikleri tercih
edilmektedir. Bu çalışmada, geçmişten günümüze kadar kısmi gölgeleme
koşullarında esnek hesaplama tabanlı maksimum güç noktası izleme tekniklerinin
kapsamlı bir derlemesi sunulmuştur. Teknikler, fotovoltaik dizi bağımlılığı,
gereken sensörler, takip verimliliği, takip hızı, algoritma karmaşıklığı ve
maksimum güç noktası etrafında salınım durumları açısından karşılaştırılmıştır. 

References

  • Ahmed, J., & Salam, Z., 2014. A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability. Applied Energy, 119, 118-130.
  • Ahmed, J., & Salam, Z., 2015. A critical evaluation on maximum power point tracking methods for partial shading in PV systems. Renewable and Sustainable Energy Reviews, 47, 933-953.
  • Amir, A., Selvaraj, J., & Rahim, N. A., 2016. Study of the MPP tracking algorithms: Focusing the numerical method techniques. Renewable and Sustainable Energy Reviews, 62, 350-371.
  • Babu, T. S., Rajasekar, N., & Sangeetha, K., 2015. Modified particle swarm optimization technique based maximum power point tracking for uniform and under partial shading condition. Applied soft computing, 34, 613-624.
  • Badis, A., Mansouri, M. N., & Sakly, A., 2016. PSO and GA-based maximum power point tracking for partially shaded photovoltaic systems. In Renewable Energy Congress (IREC), 2016 7th International (pp. 1-6). IEEE.
  • Bana, S., & Saini, R. P, 2017. Experimental investigation on power output of different photovoltaic array configurations under uniform and partial shading scenarios. Energy, 127, 438-453.
  • Bendib, B., Belmili, H., & Krim, F., 2015. A survey of the most used MPPT methods: Conventional and advanced algorithms applied for photovoltaic systems. Renewable and Sustainable Energy Reviews, 45, 637-648.
  • Belhachat, F., Larbes, C., 2015. Modeling, analysis and comparison of solar photovoltaic array configurations under partial shading conditions. Solar Energy, 120, 399-418.
  • Belhachat, F., & Larbes, C., 2017. Global maximum power point tracking based on ANFIS approach for PV array configurations under partial shading conditions. Renewable and Sustainable Energy Reviews, 77, 875-889.
  • Bhatnagar, P., & Nema, R. K., 2013. Maximum power point tracking control techniques: State-of-the-art in photovoltaic applications. Renewable and Sustainable Energy Reviews, 23, 224-241.
  • Bidram, A., Davoudi, A., & Balog, R. S., 2012. Control and circuit techniques to mitigate partial shading effects in photovoltaic arrays. IEEE Journal of Photovoltaics, 2(4), 532-546.
  • Bingöl, O., & Özkaya, B., 2018. Analysis and comparison of different PV array configurations under partial shading conditions. Solar Energy, 160, 336-343.
  • Bouilouta, A., Mellit, A., & Kalogirou, S. A., 2013. New MPPT method for stand-alone photovoltaic systems operating under partially shaded conditions. Energy, 55, 1172-1185.
  • Chu, S. C., & Tsai, P. W., 2007. Computational intelligence based on the behavior of cats. International Journal of Innovative Computing, Information and Control, 3(1), 163-173.
  • Das, S. K., Verma, D., Nema, S., & Nema, R. K., 2017. Shading mitigation techniques: State-of-the-art in photovoltaic applications. Renewable and Sustainable Energy Reviews, 78, 369-390.
  • Dileep, G., & Singh, S. N., 2017. Application of soft computing techniques for maximum power point tracking of SPV system. Solar Energy, 141, 182-202.
  • Dhivya, P., & Kumar, K. R., 2017. MPPT based control of sepic converter using firefly algorithm for solar PV system under partial shaded conditions. In Innovations in Green Energy and Healthcare Technologies (IGEHT), 2017 International Conference on IEEE, 1-8.
  • Dorigo, M., & Gambardella, L. M., 1997. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on evolutionary computation, 1(1), 53-66.
  • Eltawil, M. A., & Zhao, Z., 2013. MPPT techniques for photovoltaic applications. Renewable and Sustainable Energy Reviews, 25, 793-813.
  • Enany, M. A., Farahat, M. A., & Nasr, A., 2016. Modeling and evaluation of main maximum power point tracking algorithms for photovoltaics systems. Renewable and Sustainable Energy Reviews, 58, 1578-1586.
  • Gandomi, A. H., & Yang, X. S., 2014. Chaotic bat algorithm. Journal of Computational Science, 5(2), 224-232.
  • Guo, L., Meng, Z., Sun, Y., & Wang, L., 2016. Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm. Energy conversion and management, 108, 520-528.
  • Guo, L., Meng, Z., Sun, Y., & Wang, L., 2017. A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition. Energy.
  • Huang, C., Zhang, Z., Wang, L., Song, Z., & Long, H., 2017. A novel global maximum power point tracking method for PV system using Jaya algorithm, 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), 1-5.
  • Ishaque, K., Salam, Z., Taheri, H., & Shamsudin, A., 2011. Maximum power point tracking for PV system under partial shading condition via particle swarm optimization. In Applied Power Electronics Colloquium (IAPEC), IEEE, 5-9.
  • Ishaque, K., Salam, Z., Amjad, M., & Mekhilef, S., 2012. An improved particle swarm optimization (PSO)–based MPPT for PV with reduced steady-state oscillation. IEEE transactions on Power Electronics, 27(8), 3627-3638.
  • Ishaque, K., & Salam, Z., 2013. A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition. Renewable and Sustainable Energy Reviews, 19, 475-488.
  • Jang, J. S., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
  • Ji, Y. H., Jung, D. Y., Kim, J. G., Kim, J. H., Lee, T. W., & Won, C. Y., 2011. A real maximum power point tracking method for mismatching compensation in PV array under partially shaded conditions. IEEE Transactions on power electronics, 26(4), 1001-1009.
  • Jiang, L. L., Maskell, D. L., & Patra, J. C., 2013. A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions. Energy and Buildings, 58, 227-236.
  • Jiang, L. L., & Maskell, D. L., 2014. A uniform implementation scheme for evolutionary optimization algorithms and the experimental implementation of an ACO based MPPT for PV systems under partial shading. In Computational Intelligence Applications in Smart Grid (CIASG), IEEE, 1-8.
  • Jordehi, A. R., 2016. Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches. Renewable and Sustainable Energy Reviews, 65, 1127-1138.
  • Jumpasri, N., Pinsuntia, K., Woranetsuttikul, K., Nilsakorn, T., & Khan-ngern, W., 2014. Improved particle swarm optimization algorithm using average model on MPPT for partial shading in PV array. In Electrical Engineering Congress (iEECON), 2014 International IEEE, 1-4.
  • Kaced, K., Larbes, C., Ramzan, N., Bounabi, M., & Elabadine Dahmane, Z., 2017. Bat algorithm based maximum power point tracking for photovoltaic system under partial shading conditions. Solar Energy, 158, 490-503.
  • Kalogirou, S. A., 2001. Artificial neural networks in renewable energy systems applications: a review. Renewable and sustainable energy reviews, 5(4), 373-401.
  • Karaboga, D., & Basturk, B., 2007. Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In International fuzzy systems association world congress, Springer, 789-798.
  • Karaboga, N., 2009. A new design method based on artificial bee colony algorithm for digital IIR filters. Journal of the Franklin Institute, 346(4), 328-348.
  • Karaboga, D., 2010. Artificial bee colony algorithm. Scholarpedia, 5(3), 6915.
  • Karaboga, D., 2005. An idea based on honey bee swarm for numerical optimization Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department, 200.
  • Kennedy, J., & Eberhart, R., 1995. PSO optimization. In Proc. IEEE Int. Conf. Neural Networks, IEEE Service Center, Piscataway, NJ, 4, 1941-1948.
  • Kumar, N., Hussain, I., Singh, B., & Panigrahi, B. K., 2017. Rapid MPPT for uniformly and partial shaded PV system by using JayaDE algorithm in highly fluctuating atmospheric conditions. IEEE Transactions on Industrial Informatics, 13(5), 2406-2416.
  • Liu, Y. H., Chen, J. H., & Huang, J. W., 2015. A review of maximum power point tracking techniques for use in partially shaded conditions. Renewable and Sustainable Energy Reviews, 41, 436-453.
  • Liu, L., Meng, X., & Liu, C., 2016. A review of maximum power point tracking methods of PV power system at uniform and partial shading. Renewable and Sustainable Energy Reviews, 53, 1500-1507.
  • Malathy, S., & Ramaprabha, R. (2018). Reconfiguration strategies to extract maximum power from photovoltaic array under partially shaded conditions. Renewable and Sustainable Energy Reviews, 81, 2922-2934.
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There are 83 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Review Articles
Authors

Okan Bingöl 0000-0001-9817-7266

Burçin Özkaya This is me 0000-0002-9858-3982

Publication Date December 19, 2019
Submission Date May 28, 2019
Acceptance Date July 7, 2019
Published in Issue Year 2019 Volume: 7 Issue: 4

Cite

APA Bingöl, O., & Özkaya, B. (2019). A COMPREHENSIVE OVERVIEW OF SOFT COMPUTING BASED MPPT TECHNIQUES FOR PARTIAL SHADING CONDITIONS IN PV SYSTEMS. Mühendislik Bilimleri Ve Tasarım Dergisi, 7(4), 926-939. https://doi.org/10.21923/jesd.570887